38 citations · 41 across the 20 of their papers we have counts for
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cs.CL2024
EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation
Shih-Yang Liu, Maksim Khadkevich, Nai Chit Fung +10
While post-training compression techniques effectively reduce the memory footprint, latency, and power consumption of Large Language Models (LLMs), they often result in noticeable…
cs.CL2024★ 38 cited
DoRA: Weight-Decomposed Low-Rank Adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin +4
Among the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its variants have gained considerable popularity because of avoiding additional inference costs. Howe…